






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DeformableDetrImageProcessor
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def __init__( self   :     str						, __lowercase   :     Any						, __lowercase   :     Optional[Any]=7						, __lowercase   :     List[str]=3						, __lowercase   :     str=30						, __lowercase   :     Tuple=400						, __lowercase   :     str=True						, __lowercase   :     Dict=None						, __lowercase   :     Dict=True						, __lowercase   :     List[str]=[0.5, 0.5, 0.5]						, __lowercase   :     Any=[0.5, 0.5, 0.5]						, __lowercase   :     str=True						, __lowercase   :     List[Any]=1 / 255						, __lowercase   :     Dict=True						, )				-> Dict:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        __UpperCAmelCase				:      Any	 =    size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        __UpperCAmelCase				:      Any	 =    parent
        __UpperCAmelCase				:      List[Any]	 =    batch_size
        __UpperCAmelCase				:      Optional[Any]	 =    num_channels
        __UpperCAmelCase				:      List[Any]	 =    min_resolution
        __UpperCAmelCase				:      List[Any]	 =    max_resolution
        __UpperCAmelCase				:      Any	 =    do_resize
        __UpperCAmelCase				:      Any	 =    size
        __UpperCAmelCase				:      Dict	 =    do_normalize
        __UpperCAmelCase				:      str	 =    image_mean
        __UpperCAmelCase				:      Optional[int]	 =    image_std
        __UpperCAmelCase				:      List[str]	 =    do_rescale
        __UpperCAmelCase				:      Optional[int]	 =    rescale_factor
        __UpperCAmelCase				:      Dict	 =    do_pad
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Tuple:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }




      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Any						, __lowercase   :     Union[str, Any]=False   )				-> int:
        if not batched:
          __UpperCAmelCase				:      List[str]	 =    image_inputs[0]
          if isinstance(__lowercase						, Image.Image   ):
            __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    image.size
          else:
            __UpperCAmelCase ,					__UpperCAmelCase				:      int	 =    image.shape[1], image.shape[2]
          if w < h:
            __UpperCAmelCase				:      Union[str, Any]	 =    int(self.size["""shortest_edge"""] * h / w   )
            __UpperCAmelCase				:      Optional[Any]	 =    self.size["""shortest_edge"""]
          elif w > h:
            __UpperCAmelCase				:      Union[str, Any]	 =    self.size["""shortest_edge"""]
            __UpperCAmelCase				:      Any	 =    int(self.size["""shortest_edge"""] * w / h   )
          else:
            __UpperCAmelCase				:      int	 =    self.size["""shortest_edge"""]
            __UpperCAmelCase				:      str	 =    self.size["""shortest_edge"""]

        else:
          __UpperCAmelCase				:      Union[str, Any]	 =    []
          for image in image_inputs:
            __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
          __UpperCAmelCase				:      Optional[Any]	 =    max(__lowercase						, key=lambda __lowercase   : item[0]   )[0]
          __UpperCAmelCase				:      Optional[Any]	 =    max(__lowercase						, key=lambda __lowercase   : item[1]   )[1]

        return expected_height, expected_width


@require_torch
@require_vision
class        a       (					lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Tuple							=      DeformableDetrImageProcessor if is_vision_available() else None
      def       UpperCAmelCase ( self   :     List[Any]   )				-> List[str]:
        __UpperCAmelCase				:      str	 =    DeformableDetrImageProcessingTester(self   )
      @property
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Dict:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__lowercase						, """image_mean"""   )   )
        self.assertTrue(hasattr(__lowercase						, """image_std"""   )   )
        self.assertTrue(hasattr(__lowercase						, """do_normalize"""   )   )
        self.assertTrue(hasattr(__lowercase						, """do_resize"""   )   )
        self.assertTrue(hasattr(__lowercase						, """do_rescale"""   )   )
        self.assertTrue(hasattr(__lowercase						, """do_pad"""   )   )
        self.assertTrue(hasattr(__lowercase						, """size"""   )   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> List[str]:
        __UpperCAmelCase				:      Any	 =    self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size						, {"""shortest_edge""": 18, """longest_edge""": 1333}   )
        self.assertEqual(image_processor.do_pad						, __lowercase   )

        __UpperCAmelCase				:      int	 =    self.image_processing_class.from_dict(
            self.image_processor_dict						, size=42						, max_size=84						, pad_and_return_pixel_mask=__lowercase   )
        self.assertEqual(image_processor.size						, {"""shortest_edge""": 42, """longest_edge""": 84}   )
        self.assertEqual(image_processor.do_pad						, __lowercase   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> List[str]:
        pass
      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[int]:
        # Initialize image_processing
        __UpperCAmelCase				:      Any	 =    self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        __UpperCAmelCase				:      List[Any]	 =    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__lowercase   )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						, Image.Image   )

        # Test not batched input
        __UpperCAmelCase				:      Dict	 =    image_processing(image_inputs[0]						, return_tensors="""pt"""   ).pixel_values

        __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    self.image_processor_tester.get_expected_values(__lowercase   )

        self.assertEqual(
            encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

        # Test batched
        __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    self.image_processor_tester.get_expected_values(__lowercase						, batched=__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    image_processing(__lowercase						, return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape						, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						, )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Dict:
        # Initialize image_processing
        __UpperCAmelCase				:      Optional[int]	 =    self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        __UpperCAmelCase				:      int	 =    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__lowercase						, numpify=__lowercase   )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						, np.ndarray   )

        # Test not batched input
        __UpperCAmelCase				:      str	 =    image_processing(image_inputs[0]						, return_tensors="""pt"""   ).pixel_values

        __UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    self.image_processor_tester.get_expected_values(__lowercase   )

        self.assertEqual(
            encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

        # Test batched
        __UpperCAmelCase				:      Union[str, Any]	 =    image_processing(__lowercase						, return_tensors="""pt"""   ).pixel_values

        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    self.image_processor_tester.get_expected_values(__lowercase						, batched=__lowercase   )

        self.assertEqual(
            encoded_images.shape						, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						, )
      def       UpperCAmelCase ( self   :     Dict   )				-> Tuple:
        # Initialize image_processing
        __UpperCAmelCase				:      Optional[int]	 =    self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        __UpperCAmelCase				:      Union[str, Any]	 =    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__lowercase						, torchify=__lowercase   )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						, torch.Tensor   )

        # Test not batched input
        __UpperCAmelCase				:      Dict	 =    image_processing(image_inputs[0]						, return_tensors="""pt"""   ).pixel_values

        __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    self.image_processor_tester.get_expected_values(__lowercase   )

        self.assertEqual(
            encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

        # Test batched
        __UpperCAmelCase				:      Optional[Any]	 =    image_processing(__lowercase						, return_tensors="""pt"""   ).pixel_values

        __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    self.image_processor_tester.get_expected_values(__lowercase						, batched=__lowercase   )

        self.assertEqual(
            encoded_images.shape						, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						, )
      @slow
      def       UpperCAmelCase ( self   :     Tuple   )				-> Union[str, Any]:
        # prepare image and target
        __UpperCAmelCase				:      str	 =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						, """r"""   ) as f:
          __UpperCAmelCase				:      Optional[int]	 =    json.loads(f.read()   )

        __UpperCAmelCase				:      Union[str, Any]	 =    {"""image_id""": 39769, """annotations""": target}

        # encode them
        __UpperCAmelCase				:      Dict	 =    DeformableDetrImageProcessor()
        __UpperCAmelCase				:      List[Any]	 =    image_processing(images=__lowercase						, annotations=__lowercase						, return_tensors="""pt"""   )

        # verify pixel values
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.Size([1, 3, 800, 1066]   )
        self.assertEqual(encoding["""pixel_values"""].shape						, __lowercase   )

        __UpperCAmelCase				:      Dict	 =    torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						, __lowercase						, atol=1e-4   )   )

        # verify area
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						, __lowercase   )   )
        # verify boxes
        __UpperCAmelCase				:      Tuple	 =    torch.Size([6, 4]   )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						, __lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						, __lowercase						, atol=1e-3   )   )
        # verify image_id
        __UpperCAmelCase				:      List[str]	 =    torch.tensor([39769]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						, __lowercase   )   )
        # verify is_crowd
        __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						, __lowercase   )   )
        # verify class_labels
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor([75, 75, 63, 65, 17, 17]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						, __lowercase   )   )
        # verify orig_size
        __UpperCAmelCase				:      Any	 =    torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						, __lowercase   )   )
        # verify size
        __UpperCAmelCase				:      List[Any]	 =    torch.tensor([800, 1066]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						, __lowercase   )   )




      @slow
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[Any]:
        # prepare image, target and masks_path
        __UpperCAmelCase				:      int	 =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						, """r"""   ) as f:
          __UpperCAmelCase				:      int	 =    json.loads(f.read()   )

        __UpperCAmelCase				:      Optional[int]	 =    {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

        __UpperCAmelCase				:      Union[str, Any]	 =    pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

        # encode them
        __UpperCAmelCase				:      List[Any]	 =    DeformableDetrImageProcessor(format="""coco_panoptic"""   )
        __UpperCAmelCase				:      Tuple	 =    image_processing(images=__lowercase						, annotations=__lowercase						, masks_path=__lowercase						, return_tensors="""pt"""   )

        # verify pixel values
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.Size([1, 3, 800, 1066]   )
        self.assertEqual(encoding["""pixel_values"""].shape						, __lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						, __lowercase						, atol=1e-4   )   )

        # verify area
        __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						, __lowercase   )   )
        # verify boxes
        __UpperCAmelCase				:      Dict	 =    torch.Size([6, 4]   )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						, __lowercase   )
        __UpperCAmelCase				:      Tuple	 =    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						, __lowercase						, atol=1e-3   )   )
        # verify image_id
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor([39769]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						, __lowercase   )   )
        # verify is_crowd
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						, __lowercase   )   )
        # verify class_labels
        __UpperCAmelCase				:      Optional[int]	 =    torch.tensor([17, 17, 63, 75, 75, 93]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						, __lowercase   )   )
        # verify masks
        __UpperCAmelCase				:      Optional[Any]	 =    822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						, __lowercase   )
        # verify orig_size
        __UpperCAmelCase				:      str	 =    torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						, __lowercase   )   )
        # verify size
        __UpperCAmelCase				:      str	 =    torch.tensor([800, 1066]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						, __lowercase   )   )






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




